class_docstr: The autoencoder module where the encoder and decoder are both MLPs.
  
  :meta private:
code: def forward(self, x):
      x = torch.flatten(x, start_dim=1)
      y = self.decoder(self.encoder(x))
      return torch.norm(x - y, dim=1)
  
imports: from typing import Sequence
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.utils.misc import ProgressBar, initializer
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
label_desc: Use image node_id 2 for calling the AEModule obj's underlying member method code with example usage: obj.forward(x) and returns: torch